nanogpt

Train GPT-2 transformer models from scratch using PyTorch.

1|Updated Feb 21, 2026

Search Tags:#deep learning #nlp #gpt #educational #transformer #from scratch

Ready to run

npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill nanogpt-tianhao909

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: nanogpt
Source: https://github.com/tianhao909/AI-Research-SKILLs-cn/tree/main/01-model-architecture/nanogpt
Command: npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill nanogpt-tianhao909

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, transformers, datasets, tiktoken, wandb, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a minimal, educational implementation of the GPT architecture, allowing users to understand and experiment with transformer models from the ground up.

Core Features & Use Cases

Educational Transformer: A clean, ~300-line implementation of GPT-2.
From Scratch Training: Train models on custom datasets or reproduce GPT-2 on OpenWebText.
Use Case: A researcher wants to deeply understand how transformer models like GPT work by building and training one themselves, without the complexity of large frameworks.

Quick Start

Use the nanogpt skill to train a character-level model on the Shakespeare dataset.

Frequently Asked Questions about nanogpt

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I learn transformer architecture from scratch using PyTorch?▼

To learn transformer architecture from scratch, this Skill provides a minimalist, ~300-line PyTorch implementation of the GPT model. It focuses on clarity and understandability, allowing you to experiment with the core GPT-2 architecture without the complexity of large frameworks.

Can I train a GPT model from scratch on my own custom dataset?▼

Yes, you can train a GPT model from scratch on custom datasets. The implementation supports training models from the ground up and includes scripts to train a character-level model on the Shakespeare dataset for quick experimentation.

Do I need PyTorch and Hugging Face transformers to build a GPT model from scratch?▼

Yes, you need PyTorch and Hugging Face transformers installed. The Skill specifically depends on torch, transformers, datasets, tiktoken, and wandb to facilitate building, training, and tokenizing data for the GPT architecture.

What is the best way to understand how GPT works under the hood?▼

The best way to understand how GPT works is by building and training one yourself. This educational Skill provides a clean, minimalist implementation of the GPT transformer architecture, enabling researchers to deeply understand the model's internal mechanisms.

Can I reproduce the original GPT-2 model on OpenWebText using this implementation?▼

Yes, you can reproduce GPT-2 models on OpenWebText. The Skill is designed to enable both training from scratch on custom datasets and reproducing the GPT-2 model using the OpenWebText dataset within the PyTorch framework.

Why use a minimalist GPT implementation instead of a large deep learning framework?▼

Use a minimalist GPT implementation to eliminate the complexity of large frameworks. This approach focuses on clarity and understandability for educational purposes, making it easier to experiment with and deeply understand the transformer architecture.